Industrial Marketing Management

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The choice of contract duration in franchising networks: A transaction cost and resource-based view ⁎

Nina Gorovaiaa, , Josef Windspergerb a b

Frederick University Cyprus, 7, Y. Frederickou Str., Pallouriotissa, 1036 Nicosia, Cyprus University of Vienna, Oskar-Morgenstern-Platz 1, A-1090 Vienna, Austria

A R T I C LE I N FO

A B S T R A C T

Keywords: Franchise contract duration Transaction cost theory Resource-based view Safeguarding function Knowledge leverage function

Contract duration as component of contract design has both a transaction cost savings function by mitigating exchange hazards and a strategic value creation function by leveraging knowledge-based resources. Previous studies have not explained the value creation function of contract duration in franchising. This study addresses this research gap. Specifically, we complement the transaction cost model of contract duration by developing resource-based hypotheses to explain the knowledge leverage function of contract duration. Using resourcebased theory, we expect that intangible knowledge-based resources, such as system-specific knowhow and brand name of the franchisor, will positively influence franchise contract duration. This study contributes to the franchise and contract literature by highlighting that contract duration has – in addition to the safeguarding function - an important knowledge leverage function. Data from the German franchise sector provides support for the hypotheses.

JEL codes: D23 D80 D86 L14

1. Introduction When the franchisor sets up a contract with a franchisee, contract duration is an important component of contract design. Contract duration has two economic functions: It mitigates the costs of exchange hazards due to uncertainty and relationship-specific investments and facilitates knowledge creation and exchange in franchise networks. Previous literature in organizational economics frequently studied the safeguarding function of contract duration in the context of different industries, such as contractual relations between coal suppliers and electric utilities (Joskow, 1985, 1987, 1988; Kozhevnikova & Lange, 2009; Saussier, 1999), natural gas sector (Crocker & Masten, 1988), agricultural land leases (Yoder, Hossain, Epplin, & Doye, 2008), licensing (Antelo, 2009), and electronics industry (López-Bayón & GonzálezDías, 2010). However, few studies, mainly based on transaction cost reasoning, focused on the explanation of contract duration in franchising (e. g. Brickley, Misra, & Van Horn, 2006; Vázquez, 2007; García-Herrera & Llorca-Vivero, 2010; Chanut, Chaudey, Fadairo, & Perdreau, 2015). In transaction costs theory (TCT), the role of contracts is seen to “organize transactions so as to economize on bounded rationality, while simultaneously safeguarding them against the hazards of opportunism” (Williamson, 1985: 32). Hence contracts serve mainly as safeguarding mechanism to protect against hold-up risk under uncertainty and high

⁎

transaction-specific investments. Complementary to the transaction cost model, the resource-based theory (RBT) proposes that contract partners design contracts in order to facilitate knowledge creation and knowledge transfer. For instance, Mayer and Argyres (2004) define the role of contracts as knowledge repositories about how to work efficiently with each other and consider contract design capabilities as a source of competitive advantage (Argyres & Mayer, 2007). More recently, Mouzas and Ford (2006, 2012a, 2012b) investigate the role of contracts in leveraging knowledge-based resources. Applying knowledge-based reasoning (e.g. Grant & Baden-Fuller, 2004), contracts are platforms for learning processes that organize complex interactions to explore knowledge for joint value creation (Mouzas & Ford, 2012a; Vargo & Lusch, 2011, 2016). In this paper, we complement the transaction cost model of franchise contract duration by developing hypotheses from the resourcebased theory (RBT) (e.g. Barney, 1991; Hitt, Kai, & Carnes, 2016). While transaction cost reasoning highlights the safeguarding function under uncertainty and transaction-specific investments, resource-based reasoning explains the knowledge leverage function of contracts. Specifically, the resource-based view considers the franchise contract as knowledge creation and transfer mechanism to gain competitive advantage and enhance business performance (Mouzas, 2016) by highlighting the impact of knowledge-based resources on the franchisor's choice of contract duration. We argue that system-specific knowhow

Corresponding author. E-mail addresses: [email protected] (N. Gorovaia), [email protected] (J. Windsperger).

https://doi.org/10.1016/j.indmarman.2018.03.002 Received 21 January 2017; Received in revised form 6 March 2018; Accepted 11 March 2018

N. Gorovaia, J. Windsperger

and brand name as intangible knowledge-based resources vary positively with the length of the franchise contract. These hypotheses are tested with data from the German franchise sector. The main contribution of this study to the franchise and contract design literature is to show that contract duration as component of franchise contract design has - in addition to the safeguarding function an important knowledge leverage function for strategic value creation. Applying transaction cost and resource-based reasoning, we respond to a recent call for more research on using different theoretical perspectives to explain contract design (Combs, Ketchen Jr, Shook, & Short, 2011; Schepker, Oh, Martynov, & Poppo, 2014). The paper is organized as follows: In Section 2 we develop our research model by formulating testable hypotheses derived from the transaction costs and resource-based theories. In Section 3 we describe our empirical study and present the results. In Section 4 we discuss the research contribution and managerial implications of our study. Finally, we draw some conclusions. 2. Theory and hypotheses 2.1. Theoretical framework According to the TCT, the choice of contract duration as safeguarding mechanism depends on transaction-specific investments and uncertainty (e.g. Crocker & Masten, 1988; Jiang, Chu, & Pan, 2011; Joskow, 1985, 1988; Saussier, 1999). In franchising, several previous studies examined the impact of transaction-specific investments on contract duration (Argyres & Bercovitz, 2015; Brickley et al., 2006; García-Herrera & Llorca-Vivero, 2010; Lafontaine & Blair, 2009; Seaton, 2003; Vázquez, 2007). Chanut et al. (2015) highlighted the dynamics and adjustment costs in the determination of contract duration. In addition to the transaction cost savings function, the resourcebased rationale emphasizes the strategic value creation function of the contract design by facilitating exploration and utilization of knowledgebased resources (Das & Teng, 2000; Mouzas & Ford, 2012a, 2012b; Mouzas & Blois, 2013). The resource-based perspective argues that longer franchise contracts support the development and transfer of the intangible system-specific knowhow and brand name. Therefore, the franchisor will choose longer-term contracts as knowledge leverage mechanism, if intangible system-specific knowhow and brand name are very important for gaining competitive advantage. Our theoretical framework can be summarized as follows (see Fig. 1): Contract duration has both safeguarding and knowledge

leverage functions. Under transaction cost reasoning, the intangible knowledge-based resources (such as brand name and system-specific knowhow) are given (Williamson, 1999). Hence contract duration as safeguarding mechanism has only a transaction cost savings function. In this case, contract duration determines the efficient share of the “contractual pie” (total residual income stream) of the franchisor and franchisee (see I in Fig. 1). Under resource-based reasoning, intangible knowledge-based resources (such as system-specific knowhow and brand name) positively influence contract duration by facilitating knowledge creation due to interorganizational learning and commitment. Hence contract duration as knowledge leverage mechanism has an important strategic value creation function. In this case, the size of the “contractual pie” (total residual income stream) increases through knowledge creation and exchange under longer-term contracts (see II in Fig. 1). Finally, it is important to mention that our research framework focuses on the impact of transaction cost and resource-based variables on franchise contract duration without considering possible alignment effects between transaction cost variables, resource-based variables and contract duration as governance variable (e.g. Ghosh & John, 1999). 2.2. Hypotheses 2.2.1. Transaction cost hypotheses 2.2.1.1. Transaction-specific investments. Franchisor's and franchisees' transaction-specific investments increase the relationship-specific quasi-rents and motivate the franchisor to set up long-term contracts in order to realize high quasi-rents and avoid high renegotiation costs. First, at the beginning of a franchise relationship the franchisee pays initial fees and has to make transaction-specific investments to open an outlet, and advertise the launch of a new business. Dnes (1993) found significant levels of sunk costs due to high franchisees' transactionspecific investments. Vázquez (2007) argues that investments – referring to cost of equipment, marketing, site development, franchisee fees - expose them to hold-up risk, which can be mitigated by long-term relational contracts that facilitate collaboration (Dnes, 1993). Additional studies in franchising, e.g. Seaton (2003), Brickley et al. (2006), Lafontaine and Blair (2009), García-Herrera and LlorcaVivero (2010) and Argyres and Bercovitz (2015), investigated similar hypotheses and found a positive relationship between franchisees´ transaction-specific investments and contract duration. Second, franchisor's transaction-specific investments, such as expenses for opening franchise outlets, training and technical support at the beginning of the relationship, also have a positive effect on contract duration under longer-term contracts. Under a longer investment horizon, the franchisor can capture a higher quasi-rent stream of his investments. In addition, we expect a positive impact of the interaction between franchisor's and franchisees' transaction-specific investments on franchise contract duration. This may be explained by the fact that the franchisor will choose longer-term contracts if he expects a higher quasi-rent stream due to the bonding effect of mutual specific investments (Heide & John, 1988). Hence we can formulate: H1. Franchisees' transaction-specific investments are positively related to contract duration. H2. Franchisor's transaction-specific investments are positively related to contract duration. H2a. The interaction between franchisor's and franchisees' transactionspecific investments is positively related to contract duration.

Fig. 1. Theoretical Framework.

2.2.1.2. Environmental uncertainty. Environmental uncertainty refers to market, competitive and institutional uncertainty. According to TCT, environmental uncertainty increases the transaction costs in exchange relationships (e.g. Rindfleisch & Heide, 1997). Based on transaction cost economics reasoning, several studies examine the influence of


environmental uncertainty on the design and performance of interorganizational network relations (e.g., Geyskens, Steenkamp, & Kumar, 2006; Heide & John, 1990; Noordewier, George, & John, 1990). In an uncertain local market environment, information acquisition and contractual renegotiations are costly. The higher the environmental uncertainty, the greater the likelihood that the initial franchise agreement has to be renegotiated and adjusted, resulting in high transaction costs. Therefore, in uncertain market environments formulating more complete contracts is difficult and costly (Hendrikse, Hippman, & Windsperger, 2015), discouraging the franchisor from entering into long-term contracts (Masten & Saussier, 2000). Previous empirical studies confirmed the negative effect of environmental uncertainty on contract duration (e. g. Crocker & Masten, 1988, Saussier, 1999; Jiang et al., 2011). Hence we can formulate: H3. Environmental uncertainty is negatively related to contract duration. 2.2.2. Resource-based hypotheses According to the resource-based view of the firm, intangible resources are unique and difficult to imitate and may lead to competitive advantage of the firm (Barney, 1991; Kellermanns, Walter, Crook, Kemmerer, & Narayanan, 2014). Intangible knowledge-based resources, such as system-specific knowhow and brand name, play an important role for the success of the franchise networks (Sharma & Erramilli, 2004; Sorenson & Sørensen, 2001; Watson, Stanworth, Healeas, Purdyb, & Stanworth, 2005). Transfer of intangible resources from franchisors to franchisees results in joint value creation and requires long-term collaboration between the network partners (Mellewigt, Madhok, & Weibel, 2007). The creation and transfer of intangible system-specific knowhow and brand name in franchising cannot be managed in the traditional sense of controlling information flows (Lindblom & Tikkanen, 2010). Hence, franchise contract design plays an important role in encouraging collaboration between the network partners for knowledge creation and exchange (Cochet & Garg, 2008; Mayer & Argyres, 2004). In the following, we examine the impact of franchisor's intangible system-specific knowhow and brand name on contract duration. 2.2.2.1. Franchisor's system-specific knowhow. Intangible system-specific knowhow includes the knowledge of the business concept referring to site selection, store layout, product or service development, procurement, marketing and advertising. The more important the intangible system-specific knowhow of the franchisor for the strategic value creation, the more cooperative effort and learning is required by the franchisees to absorb and successfully apply the system-specific knowledge. Therefore, franchisor's efforts for the development of intangible system-specific knowhow are higher under longer contract duration. At the same time, franchisees benefit from long-term contracts by developing commitment and improving interorganizational learning in order to replicate the intangible system knowhow at the local outlets. Hence, we hypothesize: H4. Franchisor's intangible system-specific knowhow is positively related to contract duration. 2.2.2.2. Intangible brand name. Applying resource-based reasoning, a strong brand name leads to competitive advantage for the franchisor. The franchisor bears primary responsibility for brand name development and seeks to prevent free-riding by the franchisees, which may negatively influence product/service quality and the brand name value (Hennessy, 2003). Franchisees also share responsibility for promoting and sustaining the equity of the franchised brand (Nyadzayo, Matanda, & Ewing, 2011) and are expected to develop brand resonance – intense psychological

attachment with a brand to encourage behavior that promotes the brand value (Badrinarayanan, Suh, & Kim, 2016). Franchisees' efforts to support brand name development involve long-term contractual commitments. Long-term orientation with strong relational norms and high levels of intangible inputs are necessary for the success of franchise relationships (Blut et al., 2011; Seshadri & Mishra, 2004). Therefore, the strength of the brand name is positively associated with contract duration in order to encourage the parties' long-term commitment to the brand. Hence, we can hypothesize: H5. Brand name is positively related to contract duration. In sum, we can conclude that contract duration as component of the franchise contract design has both a transaction cost saving and strategic value creation function. Based on transaction cost reasoning, we predict that contract duration varies positively with franchise partners' transaction-specific investments and negatively with environmental uncertainty. Based on resource-based reasoning, we predict that contract duration is positively associated with intangible system-specific knowhow and brand name. These hypotheses will be tested in the following section. 3. Empirical analysis 3.1. Data collection We collected data to test the hypotheses through a survey that was mailed to the German franchise companies. First, we obtained the “Franchise Wirtschaft” - a franchise directory published in Germany, and the directory of the German Franchise Federation (DFV), that list all the franchise systems operating in the country. 786 franchise systems were identified through these directories and became a sample for our study. We dropped all the systems that had less than five outlets in Germany, and were less than two years on the market. This reduced our sample to 595 franchise companies. Second, we developed self-administered questionnaires and pretested them with several franchise professionals from the Austrian and German franchise associations. We selected the respondents based on their knowledge and expertise in the topic of research. Key informant approach for data collection was used similarly to McKendall and Wagner III (1997). Key informants were senior franchise managers who are mainly responsible for network management and franchise system growth. Thirdly, we retrieved the information about the key informants from the “Franchise Wirtschaft” directory. We mailed personally addressed questionnaires to the key informants of all 595 relevant franchise systems in Germany and received back 137 questionnaires with a response rate of 23%. Due to the missing values we used 96 completely filled questionnaires for the regression analysis. We also checked for the non-response bias. First, we compared early versus late respondents according to Armstrong and Overton (1977). The late respondents served as proxies for non-respondents. Second, we compared the respondents with non-respondents in terms of contract duration, age, size, advertising fee and royalties to determine whether non-response was a serious problem for the data. Demographic data (contract duration, year of establishment, number of outlets, royalties and advertising fees) were collected from the “Franchise Wirtschaft” directory. Independent sample t-tests were performed to compare respondents to non-respondents. There were no significant differences between the two groups and we concluded that the non-response bias was not prevalent in this data. Results of the test are presented in Table 1. To check the representability of our sample, we compared distribution of contract durations of the population with the distribution of contract durations of the dataset. The same pattern was observed in both samples with means and standard deviations in both samples being very similar. Studies using self-reported perceptual measures frequently suffer from the common method bias (Podsakoff, MacKenzie, & Podsakoff,


Table 1 Non-response bias.a Means, (SD), and counts

Contract duration Age of franchise system (years) System Size (total outlets) Advertising Fee (% of sales) Royalties (% of sales)

Population

Respondents

t-value

p-value

6.33 (3.170) N = 590 10.29 (8.261) N = 595 112.718 (431.444) N = 337 1.002 (1.497) N = 326 4.473 (6.282) N = 446

6.82 (3.281) N = 119 11.19 (8.391) N = 121 155.949 (328.376) N = 118

1.544

0.334

1.089

0.123

0.992

0.322

−0.478

0.633

1.408

0.16

0.930 (1.342) N = 127 5.442 (7.452) N = 117

a

The measures of initial fee, advertising fee, and royalties were first tested by a MANOVA to ensure independence of these variables. Table 2 CFA marker tests. Model 1. 2. 3. 4. 5.

CFA Baseline Model C Model U Model R

Chi-Square Model Comparison Tests 1. Baseline vs. Model C 2. Model C vs. Model U 3. Model U vs. Model R

χ2

df

CFI

58.717 60.742 60.280 48.843 82.747

39 42 41 32 36

0.954 0.956 0.955 0.961 0.891

Δχ2

Δdf

Chi-Square critical value; 0.05

0.462 11.437 33.904*

1 9 4

3.84 16.919 4.488

2012). Though we used three perceptual measures as our explanatory variables, our dependent variable, contract duration, is an objective measure that is less likely to be associated with common method variance (Chang, van Witteloostuijn, & Eden, 2010). On the basis of Podsakoff, MacKenzie, Lee, and Podsakoff (2003), we used Harman's single-factor test to examine whether a significant amount of common method variance exists in the data. After we conducted factor analysis on all items and extracted more than one factor with eigenvalues > 1, we felt confident that common method variance is not a serious problem in our study. Furthermore, we conducted CFA marker analysis on all the reflective scales following the procedure recommended by Williams, Hartman, and Cavazotte (2010). The analysis required specifying five different latent variable models and comparing their relative fit to each other (see Table 2). A comparison of the Model C with the Baseline model provides a test of the presence of equal method effects associated with a marker latent variable. The results indicate that method effects are not present, as the difference between the models is not statistically significant (Δχ2 = 0.462, Δdf = 1, < 3.84). Comparison of Model C with Model U was done to determine whether the impact of the method marker was equal for all the items. The results reveal that the difference between the C and U models is not statistically significant (Δχ2 = 11.437, Δdf = 9, < 16.919). Finally, a comparison of the Models U and R was done to investigate the potential biasing effect of the marker variable on factor correlations. Model R exhibits significantly worse fit than model U (Δχ2 = 33.904, Δdf = 4, > 9.488). 3.2. Measurement The measures of the variables are summarized in the Appendix.

3.2.1. Contract duration (DURATION) It is measured as years of contractually stipulated duration of the franchise contract in each franchise system. The same measurement was previously used in Vázquez (2007), and García-Herrera and LlorcaVivero (2010). 3.2.2. Franchisees' transaction-specific investments (LnINV) They refer to the sum of initial fees and the amount required to start up a new franchised outlet (in thousand €). Initial investments and initial fees are an indicator for franchisees' transaction-specific investments and were previously used in empirical studies (Brickley et al., 2006; García-Herrera & Llorca-Vivero, 2010; Windsperger, 2004). The variable was log transformed for regression purposes. 3.2.3. Franchisor's transaction-specific investments (TSInv) They refer to the level of transaction-specific investments made by the franchisors at the beginning of the franchise relationship. Franchisors were asked to evaluate on a seven-point scale to which extent they bear expenses for franchisee's training, technical support and construction of the local outlet at the beginning of the franchise relationship. The measure is compatible with previously used measures of asset specificity, e.g. Heide and John (1990) and Stump and Heide (1996), and was adapted to the franchise context. 3.2.4. Intangible system-specific knowhow (TDAYS) Consistent with the measures used in previous studies (e.g., Darr, Argote, & Epple, 1995), we use initial training days (TDAYS) as a proxy for intangible system-specific knowhow of the franchisor. The more intangible the system-specific knowhow, the more training days are required to transfer it to the franchisees (Simonin, 1999). 3.2.5. Environmental uncertainty (ENV) Based on measures used by Celly and Frazier (1996) and John and Weitz (1989), this construct was measured using a three-item scale. The franchisors were asked to provide their perceptions regarding fluctuations in the outlet level sales, unpredictability of the market, and volatility of the local economic situation. 3.2.6. Brand name (BRAND) Adopted from Combs and Ketchen (2004) and Barthélemy (2008), the strength of the brand name is measured by four items on a sevenpoint Likert scale. Franchisors were asked to rate their systems on brand strength compared to competitors, brand recognition compared to competitors, reputation for quality, and the importance of brand name for achieving competitive advantage. We used multi-item scales for measuring the brand name, franchisor's transaction-specific investments and environmental uncertainty. To check convergent and discriminant validity of the constructs we estimated the average intraconstruct correlation as a “within measure” and the average correlation of each construct's items with each other construct's items as a “between measure”. The results are presented in Table 3. The “within” average correlations are higher than the “between” average correlations, providing support of discriminant validity of these constructs. Reliability was tested by calculating Cronbach's alpha (Cronbach, 1951): BRAND = 0.826, ENV = 0.738, TSInv = 0.644. Table 3 Average within/between correlations.

TSInv BRAND ENV

TSInv

BRAND

ENV

0.3764 0.0704 0.0089

0.5422 −0.0656

0.4847

The "within" average correlations are highlighted in bold.


Table 4 Descriptive statistics and correlations.

DUR SEC SIZE AGE PRFR ENV INV TSINV BRAND TDAYS ⁎⁎⁎ ⁎⁎

MEAN

SD

MIN

MAX

DUR

SEC

SIZE

AGE

PRFR

ENV

INV

TSINV

BRAND

TDAYS

6.82 0.64 37.88 11.19 0.78 3.73 130942.4 4.47 5.68 19.92

3.28 0.48 110.29 8.39 0.28 1.36 243,644.6 1.12 1.05 23.27

1 0 1 1 0 1 600 1.67 2.5 0

20 1 850 34 1 7 2050000 7 7 120

1 0.015 0.112 0.242⁎⁎ 0.041 −0.311⁎⁎⁎ 0.483⁎⁎⁎ 0.144 0.319⁎⁎⁎ 0.326⁎⁎⁎

1 −0.075 −0.142 0.207⁎⁎ −0.045 −0.019 −0.037 −0.124 0.015

1 0.388⁎⁎⁎ −0.236⁎⁎ 0.073 0.224⁎⁎ −0.060 0.013 −0.077

1 0.121 0.087 0.140 −0.061 0.110 0.003

1 −0.068 −0.237⁎⁎ −0.149 0.006 −0.072

1 −0.153 0.089 −0.037 −0.206⁎⁎

1 0.320⁎⁎⁎ 0.335⁎⁎⁎ 0.353⁎⁎⁎

1 0.093 0.101

1 0.258⁎⁎

1

p < 0.01. p < 0.05.

3.2.7. Control variables Age of the franchise system (AGE): It is measured by the number of years since the opening of the first franchise outlet in Germany and is a proxy for inter-organizational learning and experience (Gulati & Sytch, 2008). The older the franchise company, the more the franchisor learns about the application of the system knowhow at the local markets, and the longer is the duration of the franchise contract. Size of the franchise system (SIZE): SIZE is measured as the number of employees at the headquarters of the franchisor. In larger systems, the franchisor has higher contract design capabilities and this may positively influence the length of the franchise contracts (Argyres & Mayer, 2007). Variables AGE and SIZE were log transformed. Proportion of franchised outlets (PRFR): Higher PRFR may indicate that franchisee's local market knowhow is relatively important for the creation of residual income in the franchise chain, which may require longer-term franchise contracts. Sector: “0” refers to product franchising and “1” to service franchising. Descriptive statistics and correlations are reported in Table 4. None of the correlations is seen to be large enough to indicate concern about multicollinearity. 3.3. Regression analysis We use OLS and Poisson regression analysis to test the hypotheses. The dependent variable represents the franchise contract duration (DURATION). Transaction-specific investments of the franchisee (LnINV), transaction-specific investments of the franchisor (TSInv) and environmental uncertainty (ENV) are the transaction costs variables. Training days (TDAY) and brand name (BRAND) are the resource-based variables. Additionally, we include the following control variables: age of the franchise system (AGE), size of the franchise system (SIZE), the sectoral variable (SEC) and the proportion of franchised outlets (PRFR). To test the hypotheses, we estimated the following regression equations:

DURATION = α 0 + α1lnAGE + α2lnSIZE + α3SEC + α 4PRFR + α5lnINV + α 6TSInv + α 7ENV + ε1

DURATION = α 0 + α1lnAGE + α2lnSIZE + α3SEC + α 4PRFR

(1)

+ α5lnINV + α 6TSInv + α 7ENV + α8lnINV∗TSInv + ε2

DURATION = α 0 + α1lnAGE + α2lnSIZE + α3SEC + α 4PRFR

+ α5lnINV + α 6TSInv + α 7ENV + α8lnINV∗TSInv + α 9TDAYS + α10BRAND + ε3

(2)

(3)

Eq. (1) includes transaction costs and control variables. According to the transaction costs theory, we hypothesize a positive effect of franchisee's and franchisor's transaction-specific investments and a

negative effect of environmental uncertainty on contract duration. In Eq. (2) we add an interaction term between franchisor's and franchisee's transaction specific investments. We expect that the impact of franchisor's transaction-specific investments on contract duration will increase with transaction-specific investments of the franchisee. In Eq. (3) we add the resource-based variables. According to the resource-based view, we hypothesize a positive impact of intangible system-specific knowhow and brand name on franchise contract duration. The results of the OLS regression analysis are presented in the Table 5 (Models 1, 2, and 3). First, we test the transaction cost hypotheses. Franchisee's transaction-specific investments vary positively with the contract duration, and environmental uncertainty varies negatively with the contract duration. The coefficient of franchisee's transaction-specific investments is significant providing strong support for the transaction cost theory (Hypothesis 1). The higher the level of franchisee's transaction-specific investments, the longer is the contract duration. This result is consistent with previous empirical studies on contract duration (e.g. Brickley et al., 2006; García-Herrera & LlorcaVivero, 2010; Saussier, 1999; Vázquez, 2007). The data also supports the negative impact of environmental uncertainty on contract duration (Hypothesis 3). This is compatible with previous results (e.g. Jiang et al., 2011; Saussier, 1999). The coefficient of franchisor's transactionspecific investments is positive but not statistically significant, providing insufficient support to H2. In addition, we add an interaction term between franchisor's and franchisee's transaction-specific investments in Model 2. The coefficient of the interaction term is positive and significant, providing evidence that the bonding effect of mutual investments in dedicated relationship-specific assets strengthens the positive effect of the transaction-specific investments on contract duration (H2a). Second, we add the resource-based variables in Model 3. The coefficient of brand name is positive and statistically significant, supporting the resource-based hypothesis (H5). In addition, the data provide weak support for the positive impact of system-specific knowhow on contract duration (H4). Consequently, the more important intangible brand name and system-specific knowhow for value creation in the network, the larger should be the knowledge leveraging capacity of the contract design, and the longer will be the contract duration. We can conclude that a combined application of transaction cost and resource-based theory to explain contract duration in franchise relationships increases the explanatory power of the research model from R-squared 0.335 to 0.410. In addition, the results show that the significance level of the impact of the franchisee's transaction-specific investments on contract duration decreases when adding the RBT variables (see Models 2 and 3). Furthermore, we tested the hypotheses using Poisson regression. Poisson regression is used when the dependent variable takes only nonnegative integer values. Although majority of contract durations were non-negative integer values, 8 observations in the dataset were not, so we rounded them up. A pre-test showed that there was no


Table 5 OLS and Poisson regressions. Variable

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

DV: Duration

OLS

OLS

OLS

Poisson

Poisson

Poisson

⁎⁎⁎

⁎⁎⁎

LnINV ∗ TSInv

4.176 (1.065) 0.382 (0.361) 0.193 (0.268) 0.127 (0.589) 1.600 (1.149) −0.645⁎⁎ (0.302) 1.195⁎⁎⁎ (0.319) 0.150 (0.297) –

BRAND

–

3.842 (1.041) 0.434 (0.351) 0.213 (0.260) 0.100 (0.572) 1.571 (1.114) −0.611⁎⁎ (0.293) 1.212⁎⁎⁎ (0.310) 0.140 (0.288) 0.743⁎⁎ (0.291) –

TDAYS

–

–

N Model F R2 Adjusted R2

96 5.02⁎⁎⁎ 0.285 0.228

96 5.48⁎⁎⁎ 0.335 0.274

Constant lnAGE lnSIZE SEC PRFR ENV LnINV TSInv

⁎⁎⁎

⁎⁎⁎

3.753 (1.008) 0.295 (0.351) 0.414 (0.261) 0.033 (0.558) 1.475 (1.086) −0.642⁎⁎ (0.291) 0.603⁎ (0.345) 0.215 (0.280) 0.690⁎⁎ (0.287) 0.662⁎⁎ (0.303) 0.511⁎ (0.279)

1.536 (0.158) 0.054 (0.053) 0.022 (0.037) 0.013 (0.083) 0.226 (0.166) −0.084⁎⁎ (0.042) 0.182⁎⁎⁎ (0.048) 0.017 (0.043) –

93 5.48⁎⁎⁎ 0.410 0.338

⁎⁎⁎

–

1.500 (0.159) 0.058 (0.052) 0.024 (0.037) 0.006 (0.083) 0.223 (0.167) −0.080⁎ (0.042) 0.184⁎⁎⁎ (0.048) −0.000 (0.043) 0.099⁎⁎ (0.041) –

–

–

96 Chi-squared test: 36.92⁎⁎⁎ DF: 7 Pseudo R2: 0.077

96 Chi-squared test: 42.59⁎⁎⁎ DF: 8 Pseudo R2: 0.089

1.473⁎⁎⁎ (0.161) 0.034 (0.056) 0.059 (0.039) 0.002 (0.085) 0.209 (0.174) −0.087⁎⁎ (0.043) 0.087 (0.056) 0.010 (0.044) 0.087⁎⁎ (0.042) 0.110⁎⁎ (0.049) 0.065⁎ (0.039) 93 Chi-squared test: 50.88⁎⁎⁎ DF: 10 Pseudo R2: 0.110

Values in parentheses represent standard errors. ⁎⁎⁎ p < 0.01 ⁎⁎ p < 0.05. ⁎ p < 0.1.

overdispersion in the data, so Poisson regression was chosen over negative binomial regression. The results of Poisson regression are presented in Table 5 (Models 4, 5 and 6). They are compatible with the OLS regression results providing some support of both transaction costs and resource-based theory hypotheses. 4. Discussion and implications 4.1. Findings Previous studies have mainly applied transaction cost reasoning to explain contract duration in franchising. To the best of our knowledge, this study is the first that examines the determinants of franchise contract duration by using the transaction cost and the resource-based perspective. Specifically, we analyze the influence of the safeguarding and the knowledge leverage function of contract design on the franchisor's choice of contract duration. Using data from franchise companies in Germany, we present evidence on the transaction cost and resource-based determinants of franchise contract duration. First, according to the TCT, franchisee's transaction-specific investments positively influence the contract duration, while environmental uncertainty negatively influences the contract duration. The results of our study confirm these hypotheses, which are consistent with the findings in previous studies (e.g. Brickley et al., 2006; Crocker & Masten, 1988; García-Herrera & Llorca-Vivero, 2010; Joskow, 1987; Saussier, 1999; Vázquez, 2007). On the other hand, the results of our study do not confirm the positive impact of franchisor's transaction-specific investment on contract duration. This may be due to the fact that franchisor's transaction-specific investments only generate high quasi-rents and hence positively influence contract duration when they are supported by franchisee's transaction-specific investments. As shown in Models 2 and 3 as well as Models 5 and 6, there is evidence that high franchisees' transaction-specific investments increase the positive effect of

franchisor's transaction-specific investments on contract duration. Overall, our study extends the results of previous transaction cost economics literature by including the interaction effect between franchisor's and franchisees' transaction-specific investments as determinant of contract duration. Second, based on the RBT, we argue that long-term contracts facilitate strategic value creation through knowledge leverage in franchise relationships. The empirical results show the importance of intangible knowledge-based resources as determinants of franchisor's choice of contract duration. Intangible system-specific knowhow and brand name positively impact franchise contract duration, due to the long-term commitment to develop the system-specific knowhow, to transfer it and replicate it at the local market. Hence, the resourcebased perspective highlights the importance of the long-term collaboration for the creation and transfer of intangible knowledge-based resources by focusing on the knowledge leverage function of franchise contracts. Third, when we compare the TCT and RBT results in Models 2 and 3 as well as Models 5 and 6, the findings indicate that, by adding the RBT variables to the regression equation, the significance level of the impact of franchisee's transaction-specific investments on contract duration strongly decreases. This result highlights that the RBT variables (i.e. brand name and system-specific knowhow) are more important for the franchisor's choice of contract duration than the TCT variable ‘transaction-specific investments’. 4.2. Implications This study has important implications for both researchers and franchisors. First, it extends the franchise and interorganizational network literature on contract duration by arguing that franchise contract duration (as component of contract design) has - in addition to the transaction cost savings function – an important strategic value creation


function. Since the creation and transfer of intangible knowledge are necessary for gaining competitive advantage of the franchise network, the functions of a franchise contract consist both of safeguarding transaction-specific investments under uncertainty and leveraging knowledge-based resources (Mouzas & Ford, 2012a, 2012b). Specifically, in franchise systems the capability of leveraging knowledge-based resources (such as intangible system-specific knowhow and brand name) is critical for the success of network relationships (Ostendorf, Mouzas, & Chakrabarti, 2014). This view is also compatible with Teece' study (1998) on the development and exploitation of knowledge assets in other inter-organizational relationships. Second, the study adds to the TCT of contract duration by including the interaction effect between franchisees' and franchisor's transaction-specific investments as determinants of contract duration. It provides evidence that franchisor's transaction-specific investments only influence contract duration, if they are supported by franchisees' specific investments. Third, most of the existing studies on contract duration in franchising use archival data; Vázquez (2007) is an exception. Our study is based on survey data which offers an opportunity to test new hypotheses from TCT and RBT perspectives that may not be provided by using archival data. The study has important managerial implications: Franchisors should consider both safeguarding and knowledge leverage function of contract design when determining the optimal length of franchise contracts. Based on our results, we can conclude that franchisors should design contracts with longer duration under the following conditions: high franchisor's and franchisees' transaction-specific investments, highly intangible system-specific knowhow and strong brand name. Longer contracts lessen the hold-up problem under high transactionspecific investments and facilitate the creation and transfer of intangible knowledge-based resources for strategic value creation.

hypotheses derived from TCT and RBT without addressing possible alignment effects between the transaction cost variables, resourcebased variables, and contract duration as governance variable. For instance, we may expect that the value creation and transaction cost savings effects of contract duration are interrelated: High value creation due to the development of intangible brand name and system-specific knowhow requires high transaction-specific investments of the franchise partners resulting in higher quasi-rents and lower transaction costs under longer contract duration; and higher transaction-specific investments to support upgrading of knowledge-based resources (e.g. brand name value) increase competitive advantage through value cocreation under longer contract duration. This reasoning is compatible with the governance value analysis of Ghosh and John (1999) and the “triangulation hypothesis” of Madhok (2002), highlighting that transaction characteristics (e.g. transaction-specific investments), resources (e.g. brand name assets) and governance forms (e.g. contract design) must be aligned. Furthermore, our research model does not consider a relationship lifecycle perspective of contract design that focuses on the dynamic interactions between transaction-specific investments, resources and capabilities. For instance, Bensaou and Anderson (1999) and Kang, Mahoney, and Tan (2009) show that transaction-specific investments are not given (as assumed in the traditional TC model) but influenced by interorganizational learning, capabilities and reputation spill-over effects during the relationship lifecycle. Future work has to apply an alignment model (e.g. Ghosh & John, 2005, 2012) or a relationship lifecycle perspective, in order to test the interrelations and dynamics between transaction characteristics, resource characteristics and contract design.

4.3. Limitations

Franchisor's choice of contract duration is influenced by its safeguarding and knowledge leverage functions. While transaction costs reasoning highlights the safeguarding function of contracts, the resource-based view focuses on the knowledge leverage function of contract design (Mouzas & Blois, 2013; Mouzas & Ford, 2012a; Weber & Mayer, 2011). Our results show that, in addition to the transaction cost variables, intangible knowledge-based resources – in particular franchisor's system-specific knowhow and brand name – are important factors influencing franchisor's choice of contract duration. Therefore, inter-organizational networks, such as franchise networks, involving the creation and transfer of intangible knowledge-based resources use longer-term contracts as knowledge leverage mechanism for strategic value creation. On the managerial front, our results suggest that franchisor-managers have to consider both transaction cost determinants (i.e. transaction-specific investments and environmental uncertainty) and resource-based determinants (i.e. intangible system-specific knowhow and brand name) for taking the right contract duration decision. We hope that our contribution will inspire further research on contract design in interfirm networks, such as strategic alliances, joint ventures and cooperative networks. For instance, one important unexplored issue of contract design is the relationship between contract duration and contractual completeness (López-Bayón & González-Días, 2010; Susarla, Subramanyam, & Karhade, 2010).

This study is subject to the following important limitations: While our transaction cost and resource-based variables explain 41% of the variance of contract duration, other variables not included in the study may affect contract duration in franchising. In addition to the transaction cost and resource-based explanations, variables derived from the relational governance view and the bargaining power theory may influence the duration of franchise contracts. According to the relational governance view (e.g. Dyer & Singh, 1998; Zaheer & Venkatraman, 1995), governance of franchise relationships is not only based on formal contracts, but also on informal governance mechanisms, such as trust and fairness. The relational governance perspective emphasizes that trust can function as self-enforcing mechanism that is less costly than formal contracting (Bernstein, 2015; Gilson, Sabel, & Scott, 2010; Gulati, 1995; Gurcaylilar-Yenidogan & Windsperger, 2015). We expect that trust will increase contract duration by reducing relational risk and improving information exchange. Further, based on the bargaining power perspective (e.g., Choi & Triantis, 2012; Michael, 2000), we expect that strong bargaining power of the franchisor will influence the length of the franchise contract. For instance, Brickley et al. (2006) argue that powerful franchisors may exploit franchisees by using relatively short-term contracts when they have to make large transactionspecific investments. On the other hand, Argyres and Bercovitz (2015) show that franchisors use longer-term contracts when the bargaining power of the franchisees increases with the existence of franchisee associations in the network. One important limitation of our study refers to the fact that the length of the franchise contract may not fully capture duration of the franchise relationship, as franchisors use varying renewal conditions (García-Herrera & Llorca-Vivero, 2010). A shorter initial contract (with relatively easy renewal terms) may effectively indicate a longer duration of the exchange relationship. Future research should also consider the renewal terms of franchise contracts. Finally, it is important to note that our research model is based on

5. Conclusion

Appendix A. Measures of the variables Franchise contract duration (DURATION) Years of duration of the franchise contract. Environmental uncertainty (ENV) Three items on a seven-point scale, Cronbach alpha = 0.738 1. Sales at the local markets are very unpredictable


2. It is very difficult to forecast the market development in the local markets 3. Economic environment is changing quickly in the local markets Transaction-specific investments of the franchisee (LnINV) Log of the sum of initial investment and initial fees of the franchisee. Transaction-specific investments of the franchisor (TSInv) Three items measured on a seven point scale, Cronbach alpha = 0.644 1. To what extent does the franchisor bear expenses for franchisees' training at the beginning of the contract? 2. To what extent does the franchisor bear expenses for technical support of the franchisee at the beginning of the relationship? 3. To what extent does the franchisor bear expenses for setting up the local outlet organization at the beginning of the relationship? System-specific knowhow of the franchisor, measured as initial training days (TDAYS) Number of franchisees' training days at the beginning of the franchise relationship. Brand name of the franchisor (BRAND) Four items alpha = 0.826

measured

on

a

seven-point

scale,

Cronbach

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